// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "lite/kernels/host/yolo_box_compute.h"
#include <vector>
#include "lite/backends/host/math/yolo_box.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace host {

void YoloBoxCompute::Run() {
  auto& param = Param<operators::YoloBoxParam>();
  lite::Tensor* X = param.X;
  lite::Tensor* ImgSize = param.ImgSize;
  lite::Tensor* Boxes = param.Boxes;
  lite::Tensor* Scores = param.Scores;
  std::vector<int> anchors = param.anchors;
  int class_num = param.class_num;
  float conf_thresh = param.conf_thresh;
  int downsample_ratio = param.downsample_ratio;
  bool clip_bbox = param.clip_bbox;
  float scale_x_y = param.scale_x_y;
  float bias = -0.5 * (scale_x_y - 1.);
  Boxes->clear();
  Scores->clear();
  lite::host::math::YoloBox(X,
                            ImgSize,
                            Boxes,
                            Scores,
                            anchors,
                            class_num,
                            conf_thresh,
                            downsample_ratio,
                            clip_bbox,
                            scale_x_y,
                            bias);
}

}  // namespace host
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

REGISTER_LITE_KERNEL(yolo_box,
                     kHost,
                     kFloat,
                     kNCHW,
                     paddle::lite::kernels::host::YoloBoxCompute,
                     def)
    .BindInput("X", {LiteType::GetTensorTy(TARGET(kHost))})
    .BindInput("ImgSize",
               {LiteType::GetTensorTy(TARGET(kHost), PRECISION(kInt32))})
    .BindOutput("Boxes", {LiteType::GetTensorTy(TARGET(kHost))})
    .BindOutput("Scores", {LiteType::GetTensorTy(TARGET(kHost))})
    .Finalize();
